Bhowmik-Stoker Manoshi, Scholl Laura, Khlopas Anton, Sultan Assem A, Sodhi Nipun, Moskal Joseph T, Mont Michael A, Teeny Steven M
Research - Reconstructive and Robotics, Joint Replacement Division Stryker, Mahwah, New Jersey.
Joint Replacement Division, Stryker, Mahwah, New Jersey.
Surg Technol Int. 2018 Nov 11;33:337-342.
Preoperative templating of total knee arthroplasty (TKA) components can help in choosing appropriate implant size prior to surgery. While long limb radiographs have been shown to be beneficial in assessing alignment, disease state, and previous pathology or trauma, their accuracy for size prediction has not been proven. In an attempt to improve templating precision, surgeons have looked to develop other predictive models for component size determination utilizing patient characteristics. The purpose of this study was to: 1) Identify which patient characteristics influence the tibial and femoral component sizes; 2) Construct models for size prediction; 3) Test the generated models at five different centers; and 4) Compare implant survivorship and patient characteristics between those who did or did not receive an implant within one size of the prediction.
Demographic data was collected on 741 patients (845 knees) as part of a multicenter clinical trial. Correlation between component size and patient demographic data were examined using Pearson coefficients, and significant variables were included into a multivariate-linear-regression model to determine "predicted size." Operative surgeon notes and postoperative radiographs were used to determine "actual size." Predictive equations were constructed for both femoral and tibial components and were tested at five different centers. Implant survivorship and patient characteristics were compared between those who did and did not receive an implant within one size of the prediction.
The strongest predictors of component size were height, weight, and gender (p<0.01), followed by ethnicity (p=0.03) and age (p=0.03). Predictive equations were constructed for both tibial and femoral components. The model predicted the component fit within one size in 94% (r2=0.68) and 96% (r2=0.73) of femoral and tibial components. Cases beyond ±1 sizes did not have notable device-specific adverse events with Kaplan-Meier survivorship of 100% at five years.
Demographic models are an effective tool in component size prediction prior to TKA. This model has implications in reducing the need for preoperative radiographic templating, potentially resulting in increasing surgeon efficiency and possibly reducing hospital implant inventory. This may be particularly important for ambulatory or outpatient surgery centers.
全膝关节置换术（TKA）组件的术前模板制作有助于在手术前选择合适的植入物尺寸。虽然长腿X线片已被证明在评估对线、疾病状态以及既往病理或创伤方面有益，但其在尺寸预测方面的准确性尚未得到证实。为了提高模板制作的精度，外科医生试图利用患者特征开发其他用于确定组件尺寸的预测模型。本研究的目的是：1）确定哪些患者特征会影响胫骨和股骨组件的尺寸；2）构建尺寸预测模型；3）在五个不同中心测试生成的模型；4）比较在预测尺寸的一个尺寸范围内接受或未接受植入物的患者之间的植入物生存率和患者特征。
作为一项多中心临床试验的一部分，收集了741例患者（845个膝关节）的人口统计学数据。使用Pearson系数检查组件尺寸与患者人口统计学数据之间的相关性，并将显著变量纳入多元线性回归模型以确定“预测尺寸”。手术医生记录和术后X线片用于确定“实际尺寸”。为股骨和胫骨组件构建预测方程，并在五个不同中心进行测试。比较在预测尺寸的一个尺寸范围内接受和未接受植入物的患者之间的植入物生存率和患者特征。
组件尺寸的最强预测因素是身高、体重和性别（p<0.01），其次是种族（p=0.03）和年龄（p=0.03）。为胫骨和股骨组件构建了预测方程。该模型在94%（r2=0.68）的股骨组件和96%（r2=0.73）的胫骨组件中预测组件尺寸在一个尺寸范围内合适。超出±1个尺寸的病例没有明显的特定器械不良事件，五年的Kaplan-Meier生存率为100%。
人口统计学模型是TKA术前组件尺寸预测的有效工具。该模型有助于减少术前X线模板制作的需求，可能提高外科医生的效率，并可能减少医院植入物库存。这对于门诊或门诊手术中心可能尤为重要。
